Establishing Framework for 3D Printing Porous Rock Models in Curable Resins

• Published in: Transport in porous media 2019-09, Vol.129 (2), p.431-448
• Main Author: Ishutov, Sergey
• Format: Article
• Language: English
• Subjects: 3-D printers; Accuracy; Chemical properties; Civil Engineering; Classical and Continuum Physics; Computational fluid dynamics; Contact angle; Earth and Environmental Science; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Helium; Hydrogeology; Hydrology/Water Resources; Industrial Chemistry/Chemical Engineering; Model accuracy; Organic chemistry; Physical properties; Polymers; Porosity; Porous media; Pycnometry; Replication; Resins; Sandstone; Three dimensional models; Three dimensional printing; Transport properties; Ultrasonic testing; Wettability
• ISSN: 0169-3913; 1573-1634
• DOI: 10.1007/s11242-019-01297-9

Stereolithographic 3D printing is of paramount interest for researchers studying porous media because it is possible to use resins with a wide range of physical and chemical properties. Currently, these properties can be designed and adjusted to model the interaction of solids with fluids occupying the pore space, which is not feasible with natural materials. This ability enables replicating the pore network geometry of natural rocks as well as tuning the wettability of pore network surfaces. This study presents analysis of uncured liquid resins and porous rock models 3D-printed in those resins to establish the accuracy of the stereolithographic 3D printer in repeatedly printing the same pore network, as well as the fidelity of transport properties (e.g., porosity, pore sizes, wettability) to the model design. Viscosity of uncured resins was measured to predict the physical properties of the resulting cured models. A digital model of Fontainebleau sandstone was 3D-printed at three magnifications (15-, 23-, and 30-fold) from the original tomographic volume of 1 mm 3 and in six resins (black, gray, clear, white, green, and yellow). The models were validated using the following techniques: helium pycnometry (for porosity); mercury porosimetry (for pore-throat size); and drop shape analyzer (for contact angles). Validation tests showed that green resin had the highest accuracy in replicating the rock’s pore network. White, yellow, and gray resins produced models with moderate accuracy with respect to their transport properties. Black and clear resins had the lowest accuracy and would need further analysis of their physical and chemical properties to be useful in reservoir rock replication.